DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This Office action is responsive to the communication received on 09/08/2023. The claims 1-20 are pending, of which the claim(s) 1, 8, & 15 is/are in independent form.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
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I) Claims 1- 7 provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1- 7 of copending Application No. 18/244,126 (see claims filed on 09/08/2023) (reference application).
Although the claims at issue are not identical, they are not patentably distinct from each other because the claims are obvious variants of each other for the reasons set forth below. This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Regarding claim 1:
Instant Application: 18/244,113
Co-pending Application: 18/244,126
1. A fleet of digital twin devices for controlling a multi-chamber system for substrate processing, the fleet of digital twin devices comprising:
1. A fleet of digital twin devices for controlling a multi-chamber process system for substrate processing, the fleet of digital twin devices comprising:
a plurality of digital twin devices,
wherein each digital twin device is configured to model characteristics or processes of at least one process chamber of a multi-chamber system and generating control inputs for controlling the at least one process chamber during substrate processing;
a plurality of digital twin devices capturing,
wherein each digital twin device is configured to model characteristics or processes of at least one process chamber of a multi-chamber process system and generating control inputs for controlling the at least one process chamber during substrate processing;
wherein each digital twin device, of the plurality of digital twin devices comprises one or more computational models;
wherein each digital twin device, of the plurality of digital twin devices, comprises one or more computational models;
wherein each digital twin device, of the plurality of digital twin devices, determines a first data set associated with the at least one process chamber;
wherein each digital twin device, of the plurality of digital twin devices, determines a first data set associated with the at least one process chamber of a plurality of chamber processes, and the corresponding processes for processing a plurality of substrates;
wherein the first data set comprises measurements reported by probes or sensors within the at least one process chamber;
wherein the first data set comprises measurements reported by probes or sensors within the at least one chamber process, or data collected and reported by internal sensors of the digital twin device;
wherein each digital twin device, of the plurality of digital twin devices, automatically generates a second data set that comprises the control inputs, and automatically transmits the second data set to the at least one process chamber for controlling processing of substrates by the at least one process chamber; and
wherein each digital twin device, of the plurality of digital twin devices, automatically generates a second data set that comprises the control inputs, and automatically transmits the second data set to the at least one chamber process, of the plurality of chamber processes, for controlling substrate processing by the at least one chamber process; and
wherein the second data set is automatically generated by the digital twin device based on, at least in part, the first data set, and by executing the one or more computational models of the digital twin device.
wherein the second data set is automatically generated by the digital twin device based on, at least in part, the first data set, and by executing one or more computational models of the digital twin device.
Accordingly, both the claim 1 of the instant application and the claim 1 of the co-pending application’126 substantially includes similar subject matter other than the co-pending application to include additional features such as “capturing” and “and the corresponding processes for processing a plurality of substrates.” Therefore, the claim 1 of the instant application is generic to the co-pending application’126’s claim 1 and hence is not patentably distinct therefrom.
Regarding claims 2- 7, these claims are obvious variants of the claims 2- 7 of the co-pending application 18/244,126.
II) Claims 1, 8, & 15 provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 8, & 15 of copending Application No. 18/244,104 in view of Roham ( US 20240378347 A1).
This is a provisional nonstatutory double patenting rejection.
Regarding claim 1:
Instant Application: 18/244113
Co-pending Application: 18/244,104
1. A fleet of digital twin devices for controlling a multi-chamber system for substrate processing, the fleet of digital twin devices comprising:
1. A digital twin system for controlling a physical twin chamber configured to process substrates, the digital twin system comprising:
a plurality of digital twin devices,
wherein each digital twin device is configured to model characteristics or processes of at least one process chamber of a multi-chamber system and generating control inputs for controlling the at least one process chamber during substrate processing;
a digital twin device determining characteristics of a physical twin chamber and generating control inputs for controlling the physical twin chamber;
wherein each digital twin device, of the plurality of digital twin devices comprises one or more computational models;
wherein the digital twin device comprises one or more computational models for determining the characteristics of the physical twin and for generating the control inputs;
wherein each digital twin device, of the plurality of digital twin devices, determines a first data set associated with the at least one process chamber;
a digital twin device determining characteristics of a physical twin chamber and generating control inputs for controlling the physical twin chamber
wherein the digital twin device comprises one or more computational models for determining the characteristics of the physical twin and for generating the control inputs;
wherein the digital twin device determines a first data set associated with the physical twin chamber;
wherein the first data set comprises measurements reported by probes or sensors within the at least one process chamber;
wherein the first data set comprises process data collected by sensors configured to measure attributes of the physical twin chamber;
wherein each digital twin device, of the plurality of digital twin devices, automatically generates a second data set that comprises the control inputs, and automatically transmits the second data set to the at least one process chamber for controlling processing of substrates by the at least one process chamber; and
wherein the digital twin device automatically generates a second data set based on the generated control inputs and transmits the second data set to the physical twin chamber for controlling the process performed on the substrates by the physical twin chamber
wherein the second data set is automatically generated by the digital twin device based on, at least in part, the first data set, and by executing the one or more computational models of the digital twin device.
and wherein the second data set is automatically generated by the digital twin device based on, at least in part, the first data set, and by executing the one or more computational models of the digital twin device.
Accordingly, the claim 1 of the co-pending application’104 teaches or suggests each elements of the claim except its system to include plurality of digital twin devices since the system of co-pending application’104’s claim 1 requires only “digital twin device”.
However, Roham cures the deficiencies of the co-pending application’104 as set forth below in the art rejection section in paras. [011, 025].
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to (1) combine Roham and claim 1 of the co-pending application’104 because they both related to a digital twin system for controlling a physical twin chamber and (2) modify the claim 1 of the co-pending application’104 to include missing limitations from Roham. Doing so would allow to use the “digital twin system” of co-pending application’104 to control multiple physical chambers that are well-known to be required in substrate processing art to expand the usability of the digital twin system and generate more profits for the users.
Regarding claim 8, the co-pending application’104’s claim 8 in view of Roham teaches/suggests invention of this claim for the similar reasons set forth above.
Regarding claim 15, the co-pending application’104’s claims 15 in view of Roham teaches/suggests invention of the claim 15 for the similar reasons set forth above.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Roham et al. (US 20240378347 A1, Filing Date: 2022-01-10) in view of Hilkene et al. (US 20220084842 A1).
Regarding claim 1, Roham teaches/suggests a fleet of digital twin devices1 [Computing devices that implement multiple digital twins2 like items 100s (that each receive inputs 102 to generate outputs 104) for multiple chambers of “using digital twins of process chambers” for the “digital twin 100 of a process chamber”. Here, the chamber is interpreted as to include equipment because “manufacturing equipment or components…systems or sub-systems of a chamber” in para. 061] for controlling a multi-chamber system [“process chambers”] for substrate processing, the fleet of digital twin devices comprising: ([025, 071]);
a plurality of digital twin devices [items 100s of “generating digital twins of process chambers is provided”], wherein each digital twin device is configured to model [the each digital twin 100 (fig. 1 is a sample digital twin) for multiple chambers includes various types of the model(s)] characteristics or processes of at least one process chamber [“Physical chamber 220” of fig. 2] of a multi-chamber system and generating predicted wafer characteristics 104 can be used for any suitable purposes, such as: 1) design validation; 2) process validation; and/or 3) predictive maintenance”] for
wherein each digital twin device, of the plurality of digital twin devices comprises one or more computational models [“digital twin 100 can include individual models of different locations of the process chamber”] ([089]);
wherein each digital twin device, of the plurality of digital twin devices, determines [reading of the input data 102 by the models like HFS model 210 of each digital twin 100] a first data set associated with the at least one process chamber; wherein the first data set comprises measurements [“physical sensor data”] reported by probes or sensors within the at least one process chamber ([099-0100]);
wherein each digital twin device, of the plurality of digital twin devices, automatically generates a second data set [At 452,…“generate predicted wafer characteristics using the digital twin”] that comprises the In some embodiments, digital twin 100 can take inputs 102 and can generate predicted substrate characteristics 104 as an output”. Hence, the outputs 104s are generated by the digital twin 100 itself by processing of the inputs 102s via the models like 108 -130s] generated by the digital twin device based on, at least in part, the first data set, and by executing the one or more computational models of the digital twin device ([0074, 0133-0134, 0141, 0146], Figs. 1-2).
While Roham teaches of its digital twin devices 100 receiving and processing of the first data of the sensors of the chambers to generate second data sets for the chambers, it still fails to teach the second data sets (i.e., item 104 of fig. 1) to include control data as claimed. That is, Roham does not teach the provided inputs of the digital twin to include “control inputs for controlling the at least one process chamber during substrate processing;” and the second data set to include “the control inputs,” and are used for controlling processing of substrates as claimed and shown above with strikethrough emphasis. In summary, Roham teaches all elements of the claim except those shown with strikethrough emphasis.
Hilkene cures the deficiency of Roham. Hilkene relates to a digital twin providing control inputs to a process chamber [“semiconductor processing tool 100 may comprise a chamber 105”] ([006, 019]). More specifically, Hilkene teaches a digital twin device [“exemplary computer system” shown in fig. 6 that implements the “data model server 420”] comprising one or more computational models for a chamber [chamber 105] configured to: (Figs. 5A- 5B, [019, 040-042, 047]);
model characteristics or processes of at least one process chamber and generating control inputs [“model server 420 may output a control effort 463 that modifies one or more process parameters”] for controlling the at least one process chamber during substrate processing; determines a first data set [“sensor data 469 may then be fed to the data model server 420.”] associated with the at least one process chamber, wherein the first data set comprises measurements reported by probes or sensors within the at least one process chamber ([040-041]);
automatically generates a second data set that comprises the control inputs, and automatically transmits the second data set to the at least one process chamber for controlling [“data model server 420 may output a control effort 463 that modifies one or more process parameters in the tool 400 in order to correct drift and bring the tool 400 back into a desired process window”] processing of substrates by the at least one process chamber; and wherein the second data set is automatically generated by the digital twin device based on, at least in part, the first data set, and by executing the one or more computational models of the digital twin device ([044-045, 052-053]).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to (1) combine Hilkene and Roham because they both related to a digital twin device with various models for a process chamber to process wafers/substrates and (2) modify the each digital twin devices of the Roham to include missing limitations (e.g., to have the outputs from the digital twins 100 to include control inputs to use to control the processing of the substrates) as in Hilkene to significantly better control the process of Roham (Hilkene [046]). Furthermore, doing so would improve (e.g., by correcting the drift of the digital twins and bring the tools back into a desired process window) the operating of the process chambers in Roham (Hilkene [0045]). As such, Roham in view of Hilkene teach each elements of the claim and renders invention of this claim obvious to PHOSITA.
Regarding claim 2, Roham in view of Hilkene teaches the fleet of digital twin devices of claim 1, wherein the first data set includes characteristics or properties of the substrates processed in the at least one process chamber; wherein the one or more computational models used by the digital twin device is configured to model a characteristic or property of the substrates (Roham, [055], Fig. 1; Hilkene [033]).
Regarding claim 3, Roham in view of Hilkene teaches the fleet of digital twin devices of claim 2, wherein the digital twin devices, of the plurality of digital twin devices, control and monitor interactions between the process chambers of the multi-chamber system; wherein the digital twin devices, of the plurality of digital twin devices, control and monitor [“monitoring and predicting a health status of manufacturing equipment or components of manufacturing equipment”] a plurality of tasks that are executed by the process chambers of the multi-chamber system, while the process chambers are processing substrates (Roham [011, 061, 0129]).
Regarding claim 4, Roham in view of Hilkene teaches the fleet of digital twin devices of claim 1, wherein each digital twin device, of the plurality of digital twin devices, automatically generates the second data set [the output 104 of Roham to include control effort 463 of Hilkene to bring the chamber back to desired process window] and transmits the second data set to the at least one process chamber for controlling substrate processing by the at least one process chamber contemporaneously with receiving the first data set from the at least one process chamber (Roham, Fig. 1, [081] & Hilkene [045]).
Regarding claim 5, Roham in view of Hilkene teaches the fleet of digital twin devices of claim 1, wherein the one or more computation models of the each digital twin device, of the plurality of digital twin devices, comprise a virtual model of the at least one process chamber (Roham [051] & Hilkene [049]);
wherein the virtual model is configured to model one or more of: fluid dynamics, direct Monte Carlo (DSMC) simulation, EM solvers, optical modeling tools, or direct computation of mathematical equations [“closed-form physics equations in a situation”] representing a physical process chamber of the process chambers; and wherein the digital twin device performs, using at least the virtual model, real-time monitoring and controlling of the physical process chamber of the process chambers (Roham [0061, 093]; Hilkene [0042]).
Regarding claim 6, Roham in view of Hilkene teaches the fleet of digital twin devices of claim 5, wherein the virtual model of a digital twin device of the plurality of digital twin devices:
evaluates performance of the corresponding process chamber, of the process chambers, relative to its expected or historical performance as established by prior data; compares [“the one or more outputs of the physical chamber and one or more outputs of the data model 520 are compared”] performance characteristics of the digital twin device and the corresponding process chamber, of the process chambers, to evaluate the accuracy of the virtual model to results of the corresponding process chamber; and uses evaluation of the data from both the at least one process chamber and the digital twin device to create actionable insights to improve performance of the at least one process chamber (Hilkene [051-052], Fig. 5A).
Regarding claim 7, Roham in view of Hilkene teaches the fleet of digital twin devices of claim 1, wherein the one or more computational models of the digital twin device include one or more of: models of electrical delivery, models of mechanical delivery, models of fluid delivery, or models of vacuum systems; wherein the one or more computations models capture corresponding chemical actions [“a chemical composition of a layer”] reported by subsystems; and wherein the corresponding and chemical actions include one or more of: a heat transfer, transmission of electricity, electrical pulses, EM radiation, chemical reactions, material phase, erosion, or wear due to physical contact (Roham [0097, 0104]; Hilkene [002]).
Regarding claims 8- 14, Roham in view of Hilkene teaches steps of these claims for the similar reasons set forth above in claims 1- 7.
Regarding claim 15, the rejection of claim 1 is incorporated. Roham teaches A substrate processing fleet of digital twin devices [computers that implements digital twins of the “generating digital twins of process chambers is provide”] for controlling a multi-chamber system [“generating digital twins of process chambers is provided,”] for substrate processing, comprising: a plurality of digital twin devices [computers like system 500 for each of the chambers of the “digital twins of process chambers is provide”] capturing and modeling characteristics and processes of process chambers of a multi-chamber system and generating wafer characteristics 104”] for digital twin of manufacturing equipment”, e.g., item 100 of fig. 1 for a chamber], of the plurality of digital twin devices, is associated with a corresponding process chamber, of the process chambers, and comprises one or more computational models [models shown in fig. 1] for modeling the characteristics and the processes and for generating the coupled to the processer he memory having stored instructions executable by the processor to: (Figs. 1, 5, [011, 053, 0129]);
receive, by each digital twin device, of the plurality of digital twin devices a first data set [data 102 provided to the twin 100] associated with the corresponding process chamber of the process chambers; wherein the first data set comprises measurements reported by probes or sensors and data provided by the digital twin device (Fig. 1, [074]);
automatically generate, by each digital twin device, of the plurality of digital twin devices, a second data set [“digital twin 100 can take inputs 102 and can generate predicted substrate characteristics 104 as an output”] that comprises the control inputs, and automatically transmit the second data set to the corresponding process chamber, of the process chambers, for
Roham fails to teach the features shown with strikethrough emphasis but are cured by Hilkene in paras. [045, 052] & figs. 4A, 5A- 5B for the similar reasons set forth above in claim 1.
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to (1) combine Hilkene and Roham because they both related to a digital twin device include a digital twin with various models for a process chamber to process wafers/substrates and (2) modify the each digital twin devices of the Roham to include missing limitations (e.g., to have the outputs from the digital twins 100 to include control inputs to use to control the processing of the substrates) as in Hilkene. Doing so would allow to improve (e.g., by correcting the drift of the digital twins and bring the tools back into a desired process window) the operating of the process chambers in Roham (Hilkene [0045]). As such, Roham in view of Hilkene teach each elements of the claim and renders invention of this claim obvious to PHOSITA.
Regarding claim 16, Roham in view of Hilkene teaches/suggests the substrate processing fleet system of claim 15, wherein the plurality of digital twin devices interact with process chambers, of the process chambers during the substrate processing; wherein the plurality of digital twin devices [digital twins 100 for each chambers] models the execution of a plurality of tasks performed by the process chambers as the process chambers process the substrates; wherein the first data set includes characteristics and properties [item 102] of the substrates; wherein the one or more computational models used by the digital twin devices is configured to model a characteristic or property of the substrates (Roham, fig. 1, [011, 055] & Hilkene [053]).
Regarding claim 17, Roham in view of Hilkene teaches/suggests the substrate processing fleet system of claim 15, wherein each digital twin device, of the plurality of digital twin devices, automatically generates the second data set [data 104 of Roham to include the “a control effort 463 that modifies one or more process parameters”] and transmits the second data set to the corresponding process chamber, of the process chambers, for controlling substrate processing by the corresponding process chamber, of the process chambers, contemporaneously with receiving the first data set from the corresponding process chamber (Roham Fig. 1, [075] & Hilkene [045] fig. 2).
Regarding claim 18, Roham in view of Hilkene teaches/suggests the substrate processing fleet system of claim 15, wherein the one or more computation models of the each digital twin device, of the plurality of digital twin devices, comprise a virtual model of the corresponding process chamber of the process chambers; wherein the virtual model is configured to model one or more of: fluid dynamics, direct Monte Carlo (DSMC) simulation, EM solvers, optical modeling tools, or direct computation of mathematical equations representing a physical process chamber of the process chambers; wherein the digital twin device performs, using at least the virtual model, real-time monitoring and controlling of the physical process chamber of the process chambers; and wherein the digital twin device monitors and controls the physical process chamber of the process chambers by executing one or more fast-running network models and empirically built relational data models (Roham, [042]).
Regarding claim 19, Roham in view of Hilkene teaches/suggests the substrate processing fleet system of claim 18, wherein the virtual model of a digital twin device of the plurality of digital twin devices: evaluates [“outputs of the physical chamber and one or more outputs of the data model 520 are compared.” The model’s output indicate historical performance because the models are trained with previous measured data of the chambers] performance of the corresponding process chamber, of the process chambers, relative to its expected or historical performance as established by prior data; compares [model drift detection] performance characteristics of the digital twin device and the corresponding process chamber, of the process chambers, to evaluate accuracy of the virtual model to real-world results of the corresponding process chamber; and uses evaluation of the data from both the corresponding process chamber and the digital twin device to create[ “updated data model 520 is generated”, e.g., “detected branch 577 is taken.”] actionable insights to improve performance of the corresponding process chamber (Hilkene [051-052]).
Regarding claim 20, Roham in view of Hilkene teaches/suggests the substrate processing fleet system of claim 19, wherein the one or more computational models of the digital twin device include one or more of: models of electrical delivery, models of mechanical delivery, models of fluid delivery, or models of vacuum systems; wherein the one or more computations models capture corresponding physical and chemical actions reported by subsystems (Roham [015, 040]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
1) Sharma (US 20200142365 A1) teaches a digital twin device [item 904] determines a first data set [item 908] associated with the a physical device [item 902] and automatically generates a second data set that comprises the control inputs and automatically transmits the second data set [item 910] to the at least one process chamber for controlling processing of substrates by the at least one physical device and wherein the second data set is automatically generated by the digital twin device based on, at least in part, the first data set, and by executing the one or more computational models of the digital twin device (Fig. 9 [0139-0141]).
2) Yun (US 20240274453 A1) teaches executing pluralities of the digital models [“machine learning models”] corresponding to plurali3ties of the chambers 1100 ([038-040]).
3) Ramanasankaran et al. (US 20230195066 A1) teaches a fleet of digital twin devices, the fleet of digital twin devices comprising wherein each digital twin device, of the plurality of digital twin devices, determines a first data set associated with the at least one monitored physical device; wherein each digital twin device, of the plurality of digital twin devices, automatically [“data platform 100 can set triggers and/or actions in digital twins to allow the pattern to occur automatically.”] generates a second data set that comprises the control inputs for the physical device ([074, 0193-0194]).
4) Mitrovic (US 20050071039 A1) teaches fleet of digital twin devices [simulation module 302s are computers] for controlling a multi-chamber system [tools 102s] for substrate processing, the fleet of digital twin devices comprising: (Fig. 3); a plurality of digital twin devices [simulation modules 302 implementing computers], wherein each digital twin device is configured to model characteristics or processes of at least one process chamber of a multi-chamber system and generating control inputs [reading data of the tools 102] for controlling the at least one process chamber during substrate processing; wherein each digital twin device, of the plurality of digital twin devices comprises one or more computational models; wherein each digital twin device, of the plurality of digital twin devices, determines a first data set associated with the at least one process chamber (Fig. 3, [045-048]).
Contacts
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/SANTOSH R POUDEL/ Primary Examiner, Art Unit 2115
1 Examiner notes that the spec, in para. 009 states “In the context of this disclosure, a digital twin device is a device configured to capture and model characteristics and processes of the physical twin chamber
and generate control inputs for controlling the physical twin chamber.” Hence, the digital twin device is interpreted as any computing device that includes a virtual/digital twin.
2 See paras. 051-052, “digital twin device may include one or more processors and one or
more memory units”